A Proposed Tool for Preharvest Estimation of Cabbage Yield

Abstract

Declines in cabbage (Brassica oleracea var. capitata) crop quality may result from delaying harvest to allow for greater total yield. An accurate, reliable, rapid and inexpensive method to estimate yield before harvest not requiring direct weight measurements would assist cabbage growers and handlers in harvest scheduling. Results from 3 years of study during which a tool to predict cabbage yield was developed and tested are reported here. The tool was developed using plots containing a total of 13 cabbage varieties (fresh market and processing types) planted in May to July 1999 and 2000 at the Ohio Agricultural Research and Development Center (OARDC) Vegetable Crops Research Branch in Fremont, Ohio. Exhaustive measurement of marketable yield and traits of hundreds of individual heads taken from these plots revealed simple mathematical relationships among head number, size, density, and yield. The tool was tested by comparing marketable yield predicted using a formula based on these head trait relationships to direct measures of crop yield in three different studies: 1) a factorial of nine varieties and 2 planting dates completed in Fremont in 1999, 2000, and 2001, 2) a survey of 12 commercial cabbage fields in northwestern Ohio encompassing six varieties and various planting dates and fertility regimens, in 2001, and 3) a factorial of 32 varieties and 2 planting dates (10 May, 20 June) completed in Fremont in 2001. The R2 for predicted and actual marketable yield in commercial fields and experimental plots ranged from 0.72 to 0.97. Of 510 individual estimatesof marketable yield, 48% were within 10% of actual yield values. The average quotient of predicted divided by actual marketable yield for 510 estimates made for commercial and experimental samples in 1999-2001 was 0.975. Results from this study were applied to the development of a table of potential use to crop managers in obtaining preharvest estimates of cabbage crop marketable yield. The table and its underlying assumptions are easily adjusted for local conditions.